General fundamental understanding of Flame spray pyrolysis synthesis method can be achieved through physics-based modelling of droplet combustion stage which is one of its critical stages of particle formation. The model reveals how precursor droplets interact with and respond to the thermal synthesis environment during FSP. The understanding of the droplet-thermal synthesis environment interaction facilitates the design of certain rules for the synthesis of nanomaterials in Flame spray pyrolysis, especially rules related to release of precursors, a factor that currently lacks experimental techniques for its measurement. Characterized precursor release rate is extremely important to the design of catalysts homogeneity during flame spray pyrolysis synthesis of nanomaterial. Incorporating machine learning paradigm with the modelling can be a valuable approach to further facilitate the optimization of flame synthesized material property.

Rational design is a fundamental approach to designing catalysts for enhanced performance. In addition to experimental, physics-based models of phenomena are a useful way not only to predict catalysts performance but also for design. With reaction-diffusion model, the ability to control the internal mass transfer in a single step reaction is critical for design and optimization of catalysts properties for maximal performance. In reality, however, most practical reactions occur in steps - in series or parallel - which could lead to above-unity internal effectiveness factor. Due to the limitation of Weisz criteria to unity-bounded internal effectiveness factor, the development of criteria to determine an overall factor for multiple reaction system constitute an interesting challenge. My work is engaging in the pioneering of a framework for determining overall controlling factor for (A + B -> C + D, C + B -> E+F) reaction systems, that has been utilized for theoretical design of selective methanol oxidation catalysts.